The absence of information on lateral variability in the soil is detrimental to estimating accurately the local site response in the event of an earthquake. To address this problem, the use of densely sampled seismic data together with sparsely distributed but detailed vertical soil profiles obtained from cone penetration tests (CPTs) is advantageous. This study explores the adaptation of suitable machine learning (ML) approaches to derive reliable, site- and depth-specific correlations between seismic shear-wave velocity (Vs) and cone-tip resistance (qc). Such correlation could be successfully established by combining information from seismic CPT surveys with available borehole information for the Groningen region in the Netherlands. It is found that, even over substantial distances, ML-based techniques offer site- and depth-specific correlations between Vs and qc.

Capturing the spatial variability in soil is crucial for ground response analyses in the context of seismic hazard mitigation. The lateral variability in thickness and properties of the different soil layers is one of the main factors that determines the variability of the ground motion spectrum from one location to another. The absence of such lateral variability information in the subsoil in between the locations of Cone Penetration Tests (CPTs) may be compensated by the use of more densely sampled seismic data. In this research we aim to derive a shear-wave velocity field through seismic full-waveform inversion that yields a model resolution approaching that of high-resolution seismic CPT surveys. Following this, a datadriven correlation between geophysical and geotechnical information is attempted through the application of new machine-learning-based approaches.